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A data-driven framework is presented for building magneto-elastic machine-learning interatomic potentials (ML-IAPs) for large-scale spin-lattice dynamics simulations. The magneto-elastic ML-IAPs are constructed by coupling a collective atomic spin model with an ML-IAP. Together they represent a potential energy surface from which the mechanical forces on the atoms and the precession dynamics of the atomic spins are computed. Both the atomic spin model and the ML-IAP are parametrized on data from first-principles calculations. We demonstrate the efficacy of our data-driven framework across magneto-structural phase transitions by generating a magneto-elastic ML-IAP for α-iron. The combined potential energy surface yields excellent agreement with first-principles magneto-elastic calculations and quantitative predictions of diverse materials properties including bulk modulus, magnetization, and specific heat across the ferromagnetic–paramagnetic phase transition.

Billion atom molecular dynamics (MD) using quantum-accurate machine-learning Spectral Neighbor Analysis Potential (SNAP) observed long-sought high pressure BC8 phase of carbon at extreme pressure (12 Mbar) and temperature (5,000 K). 24-hour, 4650 node production simulation on OLCF Summit demonstrated an unprecedented scaling and unmatched real-world performance of SNAP MD while sampling 1 nanosecond of physical time. Efficient implementation of SNAP force kernel in LAMMPS using the Kokkos CUDA backend on NVIDIA GPUs combined with excellent strong scaling (better than 97% parallel efficiency) enabled a peak computing rate of 50.0 PFLOPs (24.9% of theoretical peak) for a 20 billion atom MD simulation on the full Summit machine (27,900 GPUs). The peak MD performance of 6.21 Matom-steps/node-s is 22.9 times greater than a previous record for quantum-accurate MD. Near perfect weak scaling of SNAP MD highlights its excellent potential to advance the frontier of quantum-accurate MD to trillion atom simulations on upcoming exascale platforms.

Physics-informed neural network architectures have emerged as a powerful tool for developing flexible PDE solvers that easily assimilate data. When applied to problems in shock physics however, these approaches face challenges related to the collocation-based PDE discretization underpinning them. By instead adopting a least squares space-time control volume scheme, we obtain a scheme which more naturally handles: regularity requirements, imposition of boundary conditions, entropy compatibility, and conservation, substantially reducing requisite hyperparameters in the process. Additionally, connections to classical finite volume methods allows application of inductive biases toward entropy solutions and total variation diminishing properties. For inverse problems in shock hydrodynamics, we propose inductive biases for discovering thermodynamically consistent equations of state that guarantee hyperbolicity. This framework therefore provides a means of discovering continuum shock models from molecular simulations of rarefied gases and metals. The output of the learning process provides a data-driven equation of state which may be incorporated into traditional shock hydrodynamics codes.

The application of deep learning toward discovery of data-driven models requires careful application of inductive biases to obtain a description of physics which is both accurate and robust. We present here a framework for discovering continuum models from high fidelity molecular simulation data. Our approach applies a neural network parameterization of governing physics in modal space, allowing a characterization of differential operators while providing structure which may be used to impose biases related to symmetry, isotropy, and conservation form. We demonstrate the effectiveness of our framework for a variety of physics, including local and nonlocal diffusion processes and single and multiphase flows. For the flow physics we demonstrate this approach leads to a learned operator that generalizes to system characteristics not included in the training sets, such as variable particle sizes, densities, and concentration., 37 pages, 15 figures

The time scale associated with shock-induced detonation is a key property of energetic materials that remains poorly understood. Herein, we test aspects of one potential mechanism, the phonon up-pumping mechanism, where shock compression excites lattice phonon modes, transferring energy to intramolecular vibrations leading to chemical bond cleavage and reaction. Using ultrafast infrared pump–probe spectroscopy on pentaerythritol tetranitrate (PETN), we reveal sub-picosecond vibrational energy transfer (VET) from the photoexcited band at 1660 cm–1 into every other infrared-active mode in the probed frequency range 800–1800 cm–1. Energy transfer processes remain incomplete at 150 ps. Computational predictions from density functional theory are used in tandem to elucidate VET pathways in PETN.

A natural extension of the descriptors used in the Spectral Neighbor Analysis Potential (SNAP) method is derived to treat atomic interactions in chemically complex systems. Atomic environment descriptors within SNAP are obtained from a basis function expansion of the weighted density of neighboring atoms. This new formulation instead partitions the neighbor density into partial densities for each chemical element, thus leading to explicit multi-element descriptors. For $N_{elem}$ chemical elements, the number of descriptors increases as $\mathcal{O}(N_{elem}^3)$, while the computational cost of the force calculation as implemented in LAMMPS is limited to $\mathcal{O}(N_{elem}^2)$ and the favorable linear scaling in the number of atoms is retained. We demonstrate these chemically aware descriptors by producing an interatomic potential for indium phosphide capable of capturing high-energy defects that result from radiation damage cascades. This new explicit multi-element SNAP method reproduces the relaxed defect formation energies with substantially greater accuracy than weighted-density SNAP, while retaining accurate representation of the bulk indium phosphide properties., Comment: 11 pages, 4 figures

Machine learning of the quantitative relationship between local environment descriptors and the potential energy surface of a system of atoms has emerged as a new frontier in the development of interatomic potentials (IAPs). Here, we present a comprehensive evaluation of machine learning IAPs (ML-IAPs) based on four local environment descriptors-atom-centered symmetry functions (ACSF), smooth overlap of atomic positions (SOAP), the spectral neighbor analysis potential (SNAP) bispectrum components, and moment tensors-using a diverse data set generated using high-throughput density functional theory (DFT) calculations. The data set comprising bcc (Li, Mo) and fcc (Cu, Ni) metals and diamond group IV semiconductors (Si, Ge) is chosen to span a range of crystal structures and bonding. All descriptors studied show excellent performance in predicting energies and forces far surpassing that of classical IAPs, as well as predicting properties such as elastic constants and phonon dispersion curves. We observe a general trade-off between accuracy and the degrees of freedom of each model and, consequently, computational cost. We will discuss these trade-offs in the context of model selection for molecular dynamics and other applications.

We present a new quantum accurate Spectral Neighbor Analysis Potential (SNAP) machine-learning potential for simulating carbon under extreme conditions of dynamic compression (pressures up to 1 TPa and temperatures up to 10,000 K). The development of SNAP potential involves (1) the generation of the training database comprised of the consistent and meaningful set of first-principles DFT (Density Functional Theory) data for carbon materials at high pressure and temperature; (2) the robust and physically guided training of the SNAP parameters on first-principles data involving statistical data analysis; and (3) the validation of the SNAP potential in MD simulations of carbon at high PT conditions. The excellent performance of quadratic SNAP potential is demonstrated by simulating the radial distribution functions at high pressure-temperature conditions and melt curve of diamond, which were found in good agreement with DFT.

Shock-induced detonation is a key property of energetic materials (EM) that remains empirically understood. One proposed mechanism of shock-initiation in EM is “phonon up-pumping” to initiate chemical reactions, where excitation of lattice phonon modes rapidly transfers energy into intramolecular vibrations, ultimately resulting in the breaking of chemical bonds. We are developing novel ultrafast laser spectroscopy techniques to study vibrational energy transfer from phonon modes to intramolecular vibrations (phonon up-pumping), as well as competing energy transfer pathways from intramolecular vibrations to phonon modes (vibrational cooling). Through combinations of plasma- generated supercontinuum infrared, tunable near- and mid-infrared, and terahertz pulses in pump-probe spectroscopy, supplemented with ab inito simulations, we can explore the energy transfer processes on a sub-picosecond time scale to elucidate vibrational energy transfer pathways and lifetimes in EM. Herein we highlight recent progress, including the spectral and temporal characteristics of the infrared and THz sources as well as preliminary results on select EM.

The central approximation made in classical molecular dynamics simulation of materials is the interatomic potential used to calculate the forces on the atoms. Great effort and ingenuity is required to construct viable functional forms and find accurate parameterizations for potentials using traditional approaches. Machine-learning has emerged as an effective alternative approach to develop accurate and robust interatomic potentials. Starting with a very general model form, the potential is learned directly from a database of electronic structure calculations and therefore can be viewed as a multiscale link between quantum and classical atomistic simulations. Risk of inaccurate extrapolation exists outside the narrow range of time- and length-scales where the two methods can be directly compared. In this work, we use the Spectral Neighbor Analysis Potential (SNAP) and show how a fit can be produced with minimal interpolation errors which is also robust in extrapolating beyond training. To demonstrate the method, we have developed a new tungsten-beryllium potential suitable for the full range of binary compositions. Subsequently, large-scale molecular dynamics simulations were performed of high energy Be atom implantation onto the (001) surface of solid tungsten. The new machine learned W-Be potential generates a population of implantation structures consistent with quantum calculations of defect formation energies. A very shallow (, 5 Figures, 2 Tables

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Erosion of the beryllium first wall material in tokamak reactors has been shown to result in transport and deposition on the tungsten divertor. Experimental studies of beryllium implantation in tungsten indicate that mixed W–Be intermetallic deposits can form, which have lower melting temperatures than tungsten and can trap tritium at higher rates. To better understand the formation and growth rate of these intermetallics, cumulative molecular dynamics (MD) simulations of both high and low energy beryllium deposition in tungsten were performed. In both cases, a W–Be mixed material layer (MML) emerged at the surface within several nanoseconds, either through energetic implantation or a thermally-activated exchange mechanism, respectively. While some ordering of the material into intermetallics occurred, fully ordered structures did not emerge from the deposition simulations. Targeted MD simulations of the MML to further study the rate of Be diffusion and intermetallic growth rates indicate that for both cases, the gradual re-structuring of the material into an ordered intermetallic layer is beyond accessible MD time scales(⩽1 μs). However, the rapid formation of the MML within nanoseconds indicates that beryllium deposition can influence other plasma species interactions at the surface and begin to alter the tungsten material properties. Therefore, beryllium deposition on the divertor surface, even in small amounts, is likely to cause significant changes in plasma-surface interactions and will need to be considered in future studies.

Macroscopic simulations of dense plasmas rely on detailed microscopic information that can be computationally expensive and is difficult to verify experimentally. In this work, we delineate the accuracy boundary between microscale simulation methods by comparing Kohn-Sham density functional theory molecular dynamics (KS-MD) and radial pair potential molecular dynamics (RPP- MD) for a range of elements, temperature, and density. By extracting the optimal RPP from KS-MD data using force-matching, we constrain its functional form and dismiss classes of potentials that assume a constant power law for small interparticle distances. Our results show excellent agreement between RPP-MD and KS-MD for multiple metrics of accuracy at temperatures of only a few electron volts. The use of RPPs offers orders of magnitude decrease in computational cost and indicates that three-body potentials are not required beyond temperatures of a few eV. Due to its efficiency, the validated RPP-MD provides an avenue for reducing errors due to finite-size effects that can be on the order of $\sim20\%$.

We explore the possibility of nonstatistical chemical reactions in condensed-phase energetic materials via reactive molecular dynamics (MD) simulations. We characterize the response of nitromethane [CH3NO2], HMX [cyclic (CH2-NNO2)4], and PETN [C-(CH2-ONO2)4] to different types of insults: electric fields of various frequencies and strengths and direct heating at various rates. We find that nonequilibrium states can be created for short time scales when energy input targets specific vibrations through the electric fields and that equilibration eventually occurs even while the insults remain present. Interestingly, for strong fields these relaxation time scales are comparable to those of the initial chemical decomposition of the molecules. NM decomposes predominantly via bimolecular reactions, and while insults targeting specific modes lead to strong nonequilibrium states, they do not affect the kinetics associated with decomposition. PETN decomposes via the unimolecular formation of NO2 and, quite interest...

The ability of high-entropy alloys to resist radiation damage is rooted in their compositional complexity and associated high configurational entropy. In addition, grain boundaries within all alloys serve as effective sinks for defects. Using atomistic modeling, we investigated defect–grain boundary interaction mechanisms near ordered and amorphous grain boundaries in pure nickel and in a model, quaternary, high-entropy alloy (FeCoCrNi). Our results demonstrate that a combination of compositional complexity with amorphization of the grain boundary leads to much more efficient recombination and annihilation mechanisms. Coupling these two microstructural features results in the lowest amount of residual damage, indicating that these effects compound to increase radiation tolerance. These observations are rooted in locally dependent defect migration barriers in the high-entropy alloy and the strong trapping at both ordered and amorphous grain boundaries.

The Spectral Neighbor Analysis Potential (SNAP) is a classical interatomic potential that expresses the energy of each atom as a linear function of selected bispectrum components of the neighbor atoms. An extension of the SNAP form is proposed that includes quadratic terms in the bispectrum components. The extension is shown to provide a large increase in accuracy relative to the linear form, while incurring only a modest increase in computational cost. The mathematical structure of the quadratic SNAP form is similar to the embedded atom method (EAM), with the SNAP bispectrum components serving as counterparts to the two-body density functions in EAM. The effectiveness of the new form is demonstrated using an extensive set of training data for tantalum structures. Similarly to artificial neural network potentials, the quadratic SNAP form requires substantially more training data in order to prevent overfitting. The quality of this new potential form is measured through a robust cross-validation analysis.

Shock wave interactions with defects, such as pores, are known to play a key role in the chemical initiation of energetic materials. The shock response of hexanitrostilbene is studied through a combination of large scale reactive molecular dynamics and mesoscale hydrodynamic simulations. In order to extend our simulation capability at the mesoscale to include weak shock conditions (< 6 GPa), atomistic simulations of pore collapse are used to define a strain rate dependent strength model. Comparing these simulation methods allows us to impose physically-reasonable constraints on the mesoscale model parameters. In doing so, we have been able to study shock waves interacting with pores as a function of this viscoplastic material response. We find that the pore collapse behavior of weak shocks is characteristically different to that of strong shocks., 9 Pages, 7 Figures + 2 pages, 2 Figures Supplemental

We use molecular dynamics simulations to describe the chemical reactions following shock-induced collapse of cylindrical pores in the high-energy density material RDX. For shocks with particle velocities of 2 km/s we find that the collapse of a 40 nm diameter pore leads to a deflagration wave. Molecular collisions during the collapse lead to ultrafast, multistep chemical reactions that occur under nonequilibrium conditions. Exothermic products formed during these first few picoseconds prevent the nanoscale hotspot from quenching. Within 30 ps, a local deflagration wave develops; it propagates at 0.25 km/s and consists of an ultrathin reaction zone of only ∼5 nm, thus involving large temperature and composition gradients. Contrary to the assumptions in current models, a static thermal hotspot matching the dynamical one in size and thermodynamic conditions fails to produce a deflagration wave indicating the importance of nonequilibrium loading in the criticality of nanoscale hot spots. These results provide...

The algorithm developed in Cawkwell, M. J. et al. J. Chem. Theory Comput. 2012 , 8 , 4094 for the computation of the density matrix in electronic structure theory on a graphics processing unit (GPU) using the second-order spectral projection (SP2) method [ Niklasson, A. M. N. Phys. Rev. B 2002 , 66 , 155115 ] has been efficiently parallelized over multiple GPUs on a single compute node. The parallel implementation provides significant speed-ups with respect to the single GPU version with no loss of accuracy. The performance and accuracy of the parallel GPU-based algorithm is compared with the performance of the SP2 algorithm and traditional matrix diagonalization methods on a multicore central processing unit (CPU).

The purpose of this short contribution is to report on the development of a Spectral Neighbor Analysis Potential (SNAP) for tungsten. We have focused on the characterization of elastic and defect properties of the pure material in order to support molecular dynamics simulations of plasma-facing materials in fusion reactors. A parallel genetic algorithm approach was used to efficiently search for fitting parameters optimized against a large number of objective functions. In addition, we have shown that this many-body tungsten potential can be used in conjunction with a simple helium pair potential to produce accurate defect formation energies for the W-He binary system.

We study the scattering of electromagnetic waves in anisotropic energetic materials. Nonlinear light-matter interactions in molecular crystals result in frequency-conversion and polarization changes. Applied electromagnetic fields of moderate intensity can induce these nonlinear effects without triggering chemical decomposition, offering a mechanism for non-ionizing identification of explosives. We use molecular dynamics simulations to compute such two-dimensional Raman spectra in the terahertz range for planar slabs made of PETN and ammonium nitrate. We discuss third-harmonic generation and polarization-conversion processes in such materials. These observed far-field spectral features of the reflected or transmitted light may serve as an alternative tool for stand-off explosive detection., Comment: 6 pages, 6 figures, LA-UR-15-27584

We use molecular dynamics simulations with the reactive potential ReaxFF to investigate the initial reactions and subsequent decomposition in the high-energy-density material α-HMX excited thermally and via electric fields at various frequencies. We focus on the role of insult type and strength on the energy increase for initial decomposition and onset of exothermic chemistry. We find both of these energies increase with the increasing rate of energy input and plateau as the processes become athermal for high loading rates. We also find that the energy increase required for exothermic reactions and, to a lesser extent, that for initial chemical reactions depend on the insult type. Decomposition can be induced with relatively weak insults if the appropriate modes are targeted but increasing anharmonicities during heating lead to fast energy transfer and equilibration between modes that limit the effect of loading type.